TensorFlow is one of the most popular open source machine learning libraries out there, powering everything from research projects to production AI systems. But like all software, it’s not immune to bugs and security flaws. Today, we’re diving deep into a critical vulnerability, CVE-2022-41887, related to the tf.keras.losses.poisson function. We'll break down the issue in simple terms, look at concrete exploit scenarios, review the code, and explain how it was fixed.

What is CVE-2022-41887?

CVE-2022-41887 is a vulnerability in TensorFlow’s implementation of the Poisson loss used for certain machine learning models. The problem is triggered when extremely large input dimensions are used, leading to what’s called an “integer overflow.” This causes TensorFlow to crash—potentially resulting in a denial of service—when performing a certain low-level multiplication operation.

Technical Explanation (in Plain English)

When calculating the Poisson loss, TensorFlow takes two arrays: y_true (the real values) and y_pred (the predicted values). These arrays are multiplied together internally using a function called functor::mul in a module known as BinaryOp.

Here’s the catch: if the size (number of elements) produced by broadcasting these arrays together is large enough to overflow a 32-bit integer (int32), the broadcasting logic misbehaves. This causes TensorFlow to crash unexpectedly, leading to a possible denial of service if untrusted users can input data (think: machine learning as a service, cloud notebooks, etc).

Here’s an oversimplified Python-style version

import numpy as np

def poisson_loss(y_true, y_pred):
    # Internally TensorFlow tries to broadcast these
    try:
        result = y_true * y_pred  # This can trigger overflow during dimension calculation
    except Exception as e:
        print("Crashes if shape size overflows:", e)

In C++, this is closer to what’s happening

// Pseudo: BinaryOp implementation in TensorFlow
int num_elements = calculate_broadcasted_shape(y_true, y_pred); // stores in int32
// ... do operation based on num_elements
// If num_elements > 2,147,483,647 (max for int32), overflow!

If you pass in arrays with dimensions so huge the calculation exceeds 2,147,483,647 elements, you'll hit this bug.

Denial of Service (DoS):

- By supplying gigantic input shapes to a public TensorFlow backend (e.g., ML API, cloud notebook), an attacker can crash the Python process or service.
  - Example: If a web app lets users upload “training data” or “predict” with their own arrays, a malicious user could send arrays with sizes that cause overflow.

Denial of Service at Scale:

- On multi-tenant cloud ML platforms, one user could crash shared worker nodes by triggering this bug, impacting many customers.
  - No remote code execution or data corruption is possible with this bug—but causing apps to crash is still serious.

Example Exploit (Python)

import tensorflow as tf
import numpy as np

# Construct a super large array shape (this would likely be killed by the system, so use with caution!)
big_dimension = int(1.5e9)
y_true = tf.ones((big_dimension, 2))
y_pred = tf.ones((big_dimension, 2))

try:
    loss = tf.keras.losses.poisson(y_true, y_pred)
except Exception as err:
    print("Crash detected:", err)

Fix and Patch Information

TensorFlow’s development team fixed this by checking for these overflows before doing the broadcasted operation. The specifics depend on the internal Eigen library and how it manages shape math.

Patch Reference

- Commit: https://github.com/tensorflow/tensorflow/commit/c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c

Example of the Fixed Behavior

After the patch, TensorFlow will raise a clear error message if the calculation overflows, instead of crashing:

try:
    loss = tf.keras.losses.poisson(y_true, y_pred)
except Exception as err:
    print("Nice! Now we get a controlled error:", err)

You’ll see something like

Nice! Now we get a controlled error: ValueError: Broadcast shape too large

Official References

- CVE-2022-41887 @ MITRE
- GitHub Security Advisory: GHSA-2ppw-mc4m-vh2q
- TensorFlow Issue #57483
- Patched Commit
- TensorFlow Release Notes

What Should Users Do?

If you maintain software that uses TensorFlow directly, upgrade as soon as the patched versions (2.9.3, 2.10.1, or 2.11) are released.  
If you rely on cloud ML services or APIs, make sure they're using a patched TensorFlow version under the hood.

If you’re stuck on TensorFlow 2.8.x, strongly consider upgrading, or apply input size validation on your side.

Final Word

CVE-2022-41887 is a classic example of how a seemingly innocent math bug can result in a crash that attackers might abuse. TensorFlow’s quick fix has made the platform safer for everyone, but it's always a good idea to keep your dependencies up-to-date and check security advisories regularly.

If you found this post helpful, share it with your data scientist and ML engineer friends! Stay safe and happy coding.

Timeline

Published on: 11/18/2022 22:15:00 UTC
Last modified on: 11/22/2022 21:55:00 UTC